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arXiv · 2212.04351

Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms

Jonathan S. Kent
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Abstract

Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation. For design tasks, this places a hard limit on the maximum complexity of any designs produced by a neural network, which is disadvantageous if a greater allowance for complexity would result in better designs. In this paper, we introduce a methodology for taking outputs of non-finite dimension from neural networks, by learning a "neural waveform," and then taking as outputs the coefficients of its Fourier series representation. We then present experimental evidence that neural networks can learn in this setting on a toy problem.

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Jonathan S. Kent. 2022-08-17. Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms. https://arxiv.org/abs/2212.04351

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